Skip to main content
SpringerLink
Log in

Implicit Data Layout Optimization for Portable Parallel Programming in C++

  • Conference paper
  • First Online:
Parallel Computing Technologies (PaCT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12942))

Included in the following conference series:

  • 860 Accesses

Abstract

The programming process for modern parallel processors including multi-core CPUs and many-core GPUs (Graphics Processing Units) represents a significant challenge for application developers. We propose to use the widely-popular programming language C++ for parallel programming in a portable way, allowing the same program to be run on different target architectures. In this paper we extend our framework PACXX (Programming Accelerators in C++) with an additional compilation pass which simplifies data management for the programmer and makes the programming process less error-prone. These changes result in a significant reduction of execution stalls caused by memory throttling. We describe the implementation of the new data layout optimization and we report experimental results that confirm the advantages of our approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. The OpenACC Application Programming Interface (2013). openacc-standard.org, version 2.0a

  2. Bolt C++ Template Library, version 1.2 (2014)

    Google Scholar 

  3. Programming Languages - C++ (Committee Draft) (2014). isocpp.org

  4. An, P., et al.: STAPL: an adaptive, generic parallel C++ library. In: Dietz, H.G. (ed.) LCPC 2001. LNCS, vol. 2624, pp. 193–208. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-35767-X_13

    Chapter  Google Scholar 

  5. Beyer, J.C., Stotzer, E.J., Hart, A., de Supinski, B.R.: OpenMP for accelerators. In: Chapman, B.M., Gropp, W.D., Kumaran, K., Müller, M.S. (eds.) IWOMP 2011. LNCS, vol. 6665, pp. 108–121. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21487-5_9

    Chapter  Google Scholar 

  6. CUDA Vector addition example (2019). https://github.com/olcf/vector_addition_tutorials/tree/master/CUDA

  7. Haidl, M., Gorlatch, S.: PACXX: towards a unified programming model for programming accelerators using C++14. In: 2014 LLVM Compiler Infrastructure in HPC, pp. 1–11, November 2014. https://doi.org/10.1109/LLVM-HPC.2014.9

  8. Hoberock, J., Bell, N.: Thrust: A Parallel Template Library, version 1.6 (2014)

    Google Scholar 

  9. Khronos OpenCL Working Group: The OpenCL Specification, version 1.2 (2012)

    Google Scholar 

  10. Khronos SYCL Working Group: The SYCL Specification, version 2020 (2021)

    Google Scholar 

  11. Kucher, V., Fey, F., Gorlatch, S.: Unified cross-platform profiling of parallel C++ applications. In: 2018 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS), pp. 57–62 (2018)

    Google Scholar 

  12. Kucher, V., Gorlatch, S.: Towards implicit memory management for portable parallel programming in C++. In: Proceedings of the 2020 ASSE, pp. 52–56. ACM, New York (2020). https://doi.org/10.1145/3399871.3399881

  13. Kucher, V., Hunloh, J., Gorlatch, S.: Toward performance-portable finite element methods on high-performance systems. In: 2019 SigTelCom, pp. 69–73, March 2019. https://doi.org/10.1109/SIGTELCOM.2019.8696146

  14. Lattner, C.: LLVM and Clang: next generation compiler technology. In: The BSD Conference, pp. 1–2 (2008)

    Google Scholar 

  15. Li, L., Kessler, C.: VectorPU: a generic and efficient data-container and component model for transparent data transfer on GPU-based heterogeneous systems. PARMA-DITAM 2017, pp. 7–12. ACM, New York (2017). https://doi.org/10.1145/3029580.3029582

  16. Microsoft: C++ AMP: Language and Programming Model, version 1.0 (2012)

    Google Scholar 

  17. Nvidia: CUDA C Programming Guide, version 6.5 (2014)

    Google Scholar 

  18. OpenCL Vector addition example (2019). https://github.com/olcf/vector_addition_tutorials/tree/master/OpenCL

Download references

Author information

Authors and Affiliations

  1. University of Muenster, Muenster, Germany

    Vladyslav Kucher & Sergei Gorlatch

Authors
  1. Vladyslav Kucher
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sergei Gorlatch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vladyslav Kucher .

Editor information

Editors and Affiliations

  1. Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk, Russia

    Victor Malyshkin

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kucher, V., Gorlatch, S. (2021). Implicit Data Layout Optimization for Portable Parallel Programming in C++. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2021. Lecture Notes in Computer Science(), vol 12942. Springer, Cham. https://doi.org/10.1007/978-3-030-86359-3_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-86359-3_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86358-6

  • Online ISBN: 978-3-030-86359-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation